A novel NIR-image segmentation method for the precise estimation of above-ground biomass in rice crops

Colorado Julian D., Calderon Francisco, Mendez Diego, Petro Eliel, Rojas Juan P., Correa Edgar S., Mondragon Ivan F., Rebolledo Maria Camila, Jaramillo-Botero Andres. 2020. A novel NIR-image segmentation method for the precise estimation of above-ground biomass in rice crops. PloS One, 15 (10):e0239591, 20 p.

Url - jeu de données - Entrepôt autre : https://figshare.com/articles/media/The_video_is_available_as_supporting_material_in_the_online_version_of_this_article_/13052814

Quartile : Q2, Sujet : MULTIDISCIPLINARY SCIENCES

Liste HCERES des revues (en SHS) : oui

Thème(s) HCERES des revues (en SHS) : Psychologie-éthologie-ergonomie; Staps

Résumé : Traditional methods to measure spatio-temporal variations in biomass rely on a labor-intensive destructive sampling of the crop. In this paper, we present a high-throughput phenotyping approach for the estimation of Above-Ground Biomass Dynamics (AGBD) using an unmanned aerial system. Multispectral imagery was acquired and processed by using the proposed segmentation method called GFKuts, that optimally labels the plot canopy based on a Gaussian mixture model, a Montecarlo based K-means, and a guided image filtering. Accurate plot segmentation results enabled the extraction of several canopy features associated with biomass yield. Machine learning algorithms were trained to estimate the AGBD according to the growth stages of the crop and the physiological response of two rice genotypes under lowland and upland production systems. Results report AGBD estimation correlations with an average of r = 0.95 and R2 = 0.91 according to the experimental data. We compared our segmentation method against a traditional technique based on clustering. A comprehensive improvement of 13% in the biomass correlation was obtained thanks to the segmentation method proposed herein.

Mots-clés Agrovoc : biomasse aérienne, spectroscopie infrarouge, Oryza sativa, traitement d'images, imagerie multispectrale, agriculture de précision, phénotypage

Mots-clés géographiques Agrovoc : Colombie

Classification Agris : F01 - Culture des plantes
U30 - Méthodes de recherche

Champ stratégique Cirad : CTS 2 (2019-) - Transitions agroécologiques

Auteurs et affiliations

• Colorado Julian D., Pontificia Universidad Javeriana (COL) - auteur correspondant
• Calderon Francisco, Pontificia Universidad Javeriana (COL)
• Mendez Diego, Pontificia Universidad Javeriana (COL)
• Petro Eliel, CIAT (COL)
• Rojas Juan P., Pontificia Universidad Javeriana (COL)
• Correa Edgar S., Pontificia Universidad Javeriana (COL)
• Mondragon Ivan F., Pontificia Universidad Javeriana (COL)
• Rebolledo Maria Camila, CIRAD-BIOS-UMR AGAP (FRA)
• Jaramillo-Botero Andres, California Institute of Technology (USA)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/599590/)

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